Home > Research > Publications & Outputs > Towards modelling language innovation acceptanc...

Electronic data

  • ACM_proc

    Rights statement: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

    Accepted author manuscript, 774 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License


Text available via DOI:

View graph of relations

Towards modelling language innovation acceptance in online social networks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Publication date22/02/2016
Host publicationWSDM '16 Proceedings of the Ninth ACM International Conference on Web Search and Data Mining
Place of PublicationNew York
Number of pages10
ISBN (print)9781450337168
<mark>Original language</mark>English
EventWSDM'16 - CA, San Francisco, United States
Duration: 22/02/201626/02/2016


Country/TerritoryUnited States
CitySan Francisco


Country/TerritoryUnited States
CitySan Francisco


Language change and innovation is constant in online and offline communication, and has led to new words entering people’s lexicon and even entering modern day dictionaries, with recent additions of ‘e-cig’ and ‘vape’. However the manual work required to identify these ‘innovations’ is both time consuming and subjective. In this work we demonstrate how such innovations in language can be identified across two different OSN’s (Online Social Networks) through the operationalisation of known language acceptance models that incorporate relatively simplistic statistical tests. From grounding our work in language theory, we identified three statistical tests that can be applied, variation in; frequency, form and meaning; each showing different success rates across the two networks (Geo-bound Twitter sample and a sample of Reddit). These tests were also applied to different community levels within the two networks allow- ing for different innovations to be identified across different community structures over the two networks, for instance: identifying regional variation across Twitter, and variation across groupings of Subreddits, where identified example in- novations included ‘casualidad’ and ‘cym’.